Use of host lipids by the Lyme disease spirochete may lead to biomarkers

Cell lipid biology in infections: an overview

Lipids are a big family of molecules with a vast number of functions in the cell membranes, within the cytoplasm, and extracellularly. Lipid droplets (LDs) are the most common storage organelles and are present in almost every tissue type in the body. They also have structural functions serving as building blocks of cellular membranes and may be precursors of other molecules such as hormones, and lipoproteins, and as messengers in signal transduction. Fatty acids (FAs), such as sterol esters and triacylglycerols, are stored in LDs and are used in β-oxidation as fuel for tricarboxylic acid cycle (TCA) and adenosine triphosphate (ATP) generation. FA uptake and entrance in the cytoplasm are mediated by membrane receptors. After a cytoplasmic round of α- and β-oxidation, FAs are guided into the mitochondrial matrix by the L-carnitine shuttle system, where they are fully metabolized, and enter the TCA cycle. Pathogen infections may lead to impaired lipid metabolism, usage of membrane phospholipids, and LD accumulation in the cytoplasm of infected cells. Otherwise, bacterial pathogens may use lipid metabolism as a carbon source, thus altering the reactions and leading to cellular and organelles malfunctioning. This review aims to describe cellular lipid metabolism and alterations that occur upon infections.

Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease

BACKGROUND

Lyme disease is the most prevalent vector-borne disease in the US, yet its host factors are poorly understood and diagnostic tests are limited. We evaluated patients in a large health system to uncover cholesterol's role in the susceptibility, severity, and machine learning-based diagnosis of Lyme disease.

METHODS

A longitudinal health system cohort comprised 1 019 175 individuals with electronic health record data and 50 329 with linked genetic data. Associations of blood cholesterol level, cholesterol genetic scores comprising common genetic variants, and burden of rare loss-of-function (LoF) variants in cholesterol metabolism genes with Lyme disease were investigated. A portable machine learning model was constructed and tested to predict Lyme disease using routine lipid and clinical measurements.

RESULTS

There were 3832 cases of Lyme disease. Increasing cholesterol was associated with greater risk of Lyme disease and hypercholesterolemia was more prevalent in Lyme disease cases than in controls. Cholesterol genetic scores and rare LoF variants in CD36 and LDLR were associated with Lyme disease risk. Serological profiling of cases revealed parallel trajectories of rising cholesterol and immunoglobulin levels over the disease course, including marked increases in individuals with LoF variants and high cholesterol genetic scores. The machine learning model predicted Lyme disease solely using routine lipid panel, blood count, and metabolic measurements.

CONCLUSIONS

These results demonstrate the value of large-scale genetic and clinical data to reveal host factors underlying infectious disease biology, risk, and prognosis and the potential for their clinical translation to machine learning diagnostics that do not need specialized assays.

Lyme borreliosis diagnosis: state of the art of improvements and innovations

With almost 700 000 estimated cases each year in the United States and Europe, Lyme borreliosis (LB), also called Lyme disease, is the most common tick-borne illness in the world. Transmitted by ticks of the genus Ixodes and caused by bacteria Borrelia burgdorferi sensu lato, LB occurs with various symptoms, such as erythema migrans, which is characteristic, whereas others involve blurred clinical features such as fatigue, headaches, arthralgia, and myalgia. The diagnosis of Lyme borreliosis, based on a standard two-tiered serology, is the subject of many debates and controversies, since it relies on an indirect approach which suffers from a low sensitivity depending on the stage of the disease. Above all, early detection of the disease raises some issues. Inappropriate diagnosis of Lyme borreliosis leads to therapeutic wandering, inducing potential chronic infection with a strong antibody response that fails to clear the infection. Early and proper detection of Lyme disease is essential to propose an adequate treatment to patients and avoid the persistence of the pathogen. This review presents the available tests, with an emphasis on the improvements of the current diagnosis, the innovative methods and ideas which, ultimately, will allow more precise detection of LB.

Autoantibodies during infectious diseases: Lessons from malaria applied to COVID-19 and other infections

Autoimmunity is a common phenomenon reported in many globally relevant infections, including malaria and COVID-19. These and other highly inflammatory diseases have been associated with the presence of autoantibodies. The role that these autoantibodies play during infection has been an emerging topic of interest. The vast numbers of studies reporting a range of autoantibodies targeting cellular antigens, such as dsDNA and lipids, but also immune molecules, such as cytokines, during malaria, COVID-19 and other infections, underscore the importance that autoimmunity can play during infection. During both malaria and COVID-19, the presence of autoantibodies has been correlated with associated pathologies such as malarial anemia and severe COVID-19. Additionally, high levels of Atypical/Autoimmune B cells (ABCs and atypical B cells) have been observed in both diseases. The growing literature of autoimmune B cells, age-associated B cells and atypical B cells in Systemic Lupus erythematosus (SLE) and other autoimmune disorders has identified recent mechanistic and cellular targets that could explain the development of autoantibodies during infection. These new findings establish a link between immune responses during infection and autoimmune disorders, highlighting shared mechanistic insights. In this review, we focus on the recent evidence of autoantibody generation during malaria and other infectious diseases and their potential pathological role, exploring possible mechanisms that may explain the development of autoimmunity during infections.